PROJECT SUMMARY While standard biological methods such as two-dimensional, in vitro cell culture and in vivo animal models are useful to examine certain biological questions, the complexity of cancer requires a continuum of model systems. In this work, we propose to create a foundation for tumor tissue engineering by employing multiple innovative methodologies to first characterize and then build biomimetic models of the extracellular matrix (ECM) in the tumor microenvironment. The ECM is a key element in biomimicry, and particularly important to consider in the context of cancer, where the composition and architecture of the ECM change drastically with disease progression. We will specifically focus upon high-grade serous ovarian cancer (HGSOC), which is associated with a particularly poor survival rate of less than 50%. HGSOC originates in the fallopian tube, metastasizes to the ovary, and then disseminates throughout the peritoneum to organs such as the omentum and small bowel mesentery. There is a recognized for additional models to study HGSOC, and the existing models have not examined the impact of variations in ECM composition or structure. Using state-of-the-art mass spectrometry, imaging technologies and thorough immunohistochemical analysis, we will characterize differences between the ECM of normal and diseased tissues across the different stages of disease. We will utilize innovative engineering approaches including tissue engineering and microfabrication to recreate tissue-specific pathophysiology in the context of HGSOC, and examine the impact of variations in ECM composition and architecture on cellular behaviors. Through these experiments in combination with computational/statistical analysis, we will determine which of the many changes that we identify are causative for disease progression. The models we develop will have a transformative impact on the study of HGSOC, and the lessons learned from our process can be applied broadly towards the larger goal of developing pathophysiologically-relevant models of all cancer types.